While the quantitative analyses based on tissue segmentation and image matching have been successfully used for brain research, none of them are routinely used for daily clinical diagnosis or prognosis. We believe that one of the fundamental reasons is the concept of the population averaging and anatomical characterization based on single atlas is not compatible with the clinical diagnosis, in which anatomical variability is inherently huge. This makes a notion of “population representative atlas” less meaningful and accuracy of image matching based on a single atlas compromised. The multi-atlas approach could be more robust in terms of registration accuracy. However, this technique has been developed to achieve better accuracy for image parcellation, which requires many pre-parcellated images. If the anatomical variability of the target population, in this case clinical populations, is large, the number of pre-parcellated atlases also needs to be large, which would pose practical problems; how to generate so many atlases with accurate pre-parcellation, which often requires manual delineations.
What would be a more fundamental problem for all the segmentation/parcellation and image-mapping approaches is, the final outcomes are mere quantitative characterization of anatomy, which does not directly provide clinically meaningful information; for example, the volume of hippocampus per se is not clinically useful. Everybody has different hippocampal volumes. There are functionally healthy elderly people with a small hippocampus volume at only 5% percentile while an Alzheimer's disease patient could have a 20 percentile hippocampus volume.